Pearls, Pitfalls, and Conditions that Mimic Mesenteric Ischemia at CT
Bibliographic record
Abstract
Acute mesenteric ischemia (AMI) is a life-threatening condition with a high mortality rate. The diagnosis of AMI is challenging because patient symptoms and laboratory test results are often nonspecific. A high degree of clinical and radiologic suspicion is required for accurate and timely diagnosis. CT angiography of the abdomen and pelvis is the first-line imaging test for suspected AMI and should be expedited. A systematic “inside-out” approach to interpreting CT angiographic images, beginning with the bowel lumen and proceeding outward to the bowel wall, mesentery, vasculature, and extraintestinal viscera, provides radiologists with a practical framework to improve detection and synthesis of imaging findings. The subtypes of AMI are arterial and venoocclusive disease, nonocclusive ischemia, and strangulating bowel obstruction; each may demonstrate specific imaging findings. Chronic mesenteric ischemia is more insidious at onset and almost always secondary to atherosclerosis. Potential pitfalls in the diagnosis of AMI include mistaking pneumatosis as a sign that is specific for AMI and not an imaging finding, misinterpretation of adynamic ileus as a benign finding, and pseudopneumatosis. Several enterocolitides can mimic AMI at CT angiography, such as inflammatory bowel disease, infections, angioedema, and radiation-induced enterocolitis. Awareness of pitfalls, conditions that mimic AMI, and potential distinguishing clinical and imaging features can assist radiologists in making an early and accurate diagnosis of AMI. ©RSNA, 2020
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".